Off-line model reduction for on-line linear MPC of nonlinear large-scale distributed systems

Xie, Weiguo, Bonis, Ioannis and Theodoropoulos, Constantinos (2011) Off-line model reduction for on-line linear MPC of nonlinear large-scale distributed systems. Computers and Chemical Engineering, 35 5: 750-757. doi:10.1016/j.compchemeng.2011.01.023


Author Xie, Weiguo
Bonis, Ioannis
Theodoropoulos, Constantinos
Title Off-line model reduction for on-line linear MPC of nonlinear large-scale distributed systems
Journal name Computers and Chemical Engineering   Check publisher's open access policy
ISSN 0098-1354
1873-4375
Publication date 2011-05
Sub-type Article (original research)
DOI 10.1016/j.compchemeng.2011.01.023
Volume 35
Issue 5
Start page 750
End page 757
Total pages 8
Place of publication Kidlington, Oxford, United Kingdom
Publisher Pergamon
Collection year 2012
Language eng
Abstract Model predictive control (MPC) is an efficient method for the controller design of a large number of processes. Model predictive control (MPC) is an efficient method for the controller design of a large number of processes. However, linear MPC is often inappropriate for controlling nonlinear large-scale systems, while non-linear MPC can be computationally costly. The resulting optimization-based procedure can lead to local minima due to the, non-convexities that non-linear systems can exhibit. To overcome the excessive computational cost of MPC application for large-scale nonlinear systems, model reduction methodology in conjunction with efficient system linearizations have been exploited to enable the efficient application of linear MPC for nonlinear distributed parameter systems (DPS). An off-line model reduction technique, the proper orthogonal decomposition (POD) method, combined with a finite element Galerkin projection is first used to extract accurate non-linear low-order models from the large-scale ones. Trajectory Piecewise-Linear (TPWL) methodologies are subsequently developed to construct a piecewise linear representation of the reduced nonlinear model, both in a static and in a dynamic fashion. Linear MPC, based on quadratic programming, can then be efficiently performed on the resulting low-order, piece-wise affine system. Our combined methodology is readily applicable in combination with advanced MPC methodologies such as multi-parametric MPC (MP-MPC) (Pistikopoulos, 2009). The stabilisation of the oscillatory behaviour of a tubular reactor with recycle is used as an illustrative example to demonstrate our methodology.
Keyword Model reduction
Model predictive control
Distributed systems
Proper orthogonal decomposition
Trajectory piecewise-linear
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status Non-UQ
Additional Notes Selected Papers from ESCAPE-20 (European Symposium of Computer Aided Process Engineering - 20), 6-9 June 2010, Ischia, Italy

Document type: Journal Article
Sub-type: Article (original research)
Collections: Julius Kruttschnitt Mineral Research Centre Publications
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Created: Tue, 11 Oct 2011, 09:23:33 EST by Karen Holtham on behalf of Julius Kruttschnitt Mineral Research Centre